Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework

Brain tumors, often referred to as intracranial tumors, are abnormal tissue masses that arise from rapidly multiplying cells. During medical imaging, it is essential to separate brain tumors from healthy tissue. The goal of this paper is to improve the accuracy of separating tumorous regions from healthy tissues in medical imaging, specifically for brain tumors in MRI images which is difficult in the field of medical image analysis. In our research work, we propose IC-Net (Inverted-C), a novel semantic segmentation architecture that combines elements from various models to provide effective and precise results. The architecture includes Multi-Attention (MA) blocks, Feature Concatenation Networks (FCN), Attention-blocks which performs crucial tasks in improving brain tumor segmentation. MA-block aggregates multi-attention features to adapt to different tumor sizes and shapes. Attention-block is focusing on key regions, resulting in more effective segmentation in complex images. FCN-block captures diverse features, making the model more robust to various characteristics of brain tumor images. Our proposed architecture is used to accelerate the training process and also to address the challenges posed by the diverse nature of brain tumor images, ultimately leads to potentially improved segmentation performance. IC-Net significantly outperforms the typical U-Net architecture and other contemporary effective segmentation techniques. On the BraTS 2020 dataset, our IC-Net design obtained notable outcomes in Accuracy, Loss, Specificity, Sensitivity as 99.65, 0.0159, 99.44, 99.86 and DSC (core, whole, and enhancing tumors as 0.998717, 0.888930, 0.866183) respectively.

Over the years, numerous approaches have been proposed for BTS, and CNNs have emerged as a powerful tool in this domain.The U-Net architecture, introduced by Ronneberger et al. has outperformed and widespread attention for its exceptional performance in biomedical image segmentation tasks, including BTS 11 .According to the study, which tests networks with higher levels of depth using small (3× 3) convolution filters, improving networks to 16-19 weight layers could result in a significant improvement 12 .In the study, the Edge U-Net model is used, which is a deep CNN that can accurately find the location of tumors by combining boundary-related MRI data with brain MRIs and a loss function 13 .The study was illustrated automatic BTS using hybrid filters and 3D medical pictures.The U-Net model is used for semantic segmentation, and 2D MRIs are used to view the tumor.The method is computationally and memory-efficient, proving to be the optimal choice for segmenting brain images 14 .
Baid, U. et al were designed a method for glioma tumor segmentation and survival prediction using a Deep Learning Radiomics Algorithm for Gliomas (DRAG) model and a 3D patch-based U-Net model.The model achieved 57.1% accuracy on the BraTS 2018 validation dataset, performing well and achieving the overall survival prediction task 15 .The model uses the MICCAI BRATS2018 to classify tumor types in MRI images based on their masses.It achieves significant results in segmenting tumors using DL approaches, with dice coefficient values for high-grade glioma volumes and low-grade glioma volumes of 0.9795 and 0.9950 respectively 16 .The research has examined an SPP-U-Net, a model that replaces residual connections with Spatial Pyramid Pooling and Attention blocks, enhancing reconstruction scope and context.It achieves comparable results without changing parameters over larger dimensions 17 .Sunita Roy, et al. proposed two new CNN-based models, S-Net and SA-Net, for image segmentation in medical imaging, particularly for brain tumors in MRI scans.These models use U-Net as the base architecture and leverage 'Merge Block' and ' Attention Block' concepts 18 .
Ruba,T, et al proposed a JGate-AttResU-Net network design for a reliable BTS system, enhancing tumor localization and generating competitive outcomes using the BRATS 2015 and 2019 datasets 19,20 .BrainNET is a new network that uses DL networks to automate the detection and classification of brain tumors from MRI images, overcoming the complexity and variance of tumors and medical data in clinical routines 21 .Yanjun Peng and Jindong Sun suggested an automatic weighted dilated convolutional network (AD-Net) for learning multimodal brain tumor features through channel feature separation learning.The AD unit uses dual-scale convolutional feature maps, two learnable parameters, and deep supervision training to quickly fit the data using the BraTS2020 dataset 22 . 23TransBTS validates the efficiency of a Transformer-based framework for visual tracking, emphasizing attention mechanisms for healthy feature learning.
Rehan Raza, et al. introduced a 3D BTS framework, a hybrid of deep residual networks and U-Net models, that significantly enhances the segmentation performance of brain tumor sub-regions when compared to stateof-the-art techniques 24 .The 2D U-Net network, trained on the BraTS datasets, can identify four areas for BTS.It can set up multiple encoder and decoder routes for various image usages.Image segmentation is used to reduce computational time.Experiments on the BraTS datasets demonstrate the model's effectiveness 25 .This study presented a fully automated 2D U-net architecture on the BraTS2020 for detecting tumor regions in healthy tissue.After experimenting with all MRI sequences, the model achieved an accuracy of 99.41%, demonstrating its effectiveness.The model is further trained to assess its robustness and performance consistency 26 .N. Phani Bindu and P. Narahari Sastry, improve accuracy, cross-or skip-connections between network blocks can be introduced.This approach improves model accuracy and performance compared to traditional U-Net models, as it eliminates the need for frequent skip connections 27 .
The study employs a model for medical image semantic segmentation using CNNs.The model eliminates semantic ambiguity in skip connection operations by adding attention gates, combining local features with global dependencies, and using multi-scale predictive fusion 36 .A modified U-Net structure using residual networks and sub-pixel convolution was proposed, enhancing modelling capability and avoiding de-convolution overlapping.The model was evaluated on the BTS dataset, achieving accuracies of 93.40% and 92.20% and outperforming existing approaches in tumor subregion classification 37 .Amin, Javeria, et al proposed model uses 07 layers for classification, including convolutional, ReLU, and softmax layers.It divides MR images into patches and assigns labels.Experiments were conducted on eight large-scale datasets, and results were validated on accuracy, sensitivity, specificity, precision, and similarity index 38 .Raut, Gajendra, et al. classify new input images as tumorous or normal, use back propagation for accuracy and autoencoders for irrelevant features.The K-Means algorithm is used for further tumor region segmentation 39 .
The study uses the 3D-U-Net model for volumetric segmentation of MRI images and tumor classification using CNNs.Validity is established through loss and precision diagrams.Performance is measured and compared, finding the proposed work more efficacious than state-of-the-art techniques 40 .Agrawal P, Katal N, Hooda N. introduce a fully automatic method for separating brain tumours using U-Net-based deep convolutional networks.This method was tested on BRATS 2015 datasets with 220 high-grade tumour cases and 54 low-grade tumour cases, and cross-validation showed that it worked well for separating tumours 41 .This paper presents a DL method for segmenting brain tumours into subregions using a multitask framework and a three-stage cascaded framework for simultaneous and sequential segmentation 42 .
The study explores an attention-based U-Net for brain tumor MRI scans.The model uses U-Nets to segment glioma subregions using T1, T2, T1CE, and FLAIR modalities.The model segmented WT, TC, and ET using the FLAIR modality, achieving scores of 95.56, 93.31, and 89.95 over BraTS 2018 28 .Feng, Xue, et al. proposed a patch-based 3D UNet with an attention block.Findings are mean Dice scores of 0.806 (ET), 0.863 (TC), and 0.918 (WT) in the validation dataset 29 .Na Li and Kai Ren developed DAU-Net, an attention-based nested segmentation network.A deep supervised encoder-decoder architecture and a redesigned dense skip connection to identify key feature regions and merge extracted features 33 .A large-kernel (LK) attention module that integrates convolution, self-attention, and channel adaptation was proposed by Li, Hao, et al. for efficient multi-organ and tumor segmentation 31 .SCAU-Net is a 3D U-Net model for brain tumor segmentation, employing external attention and selfcalibrated convolution modules.It achieves competitive results on the BraTS 2020, 2018 and 2019 validation datasets 32 .In this work, a 3D U-Net model that uses different skip connections with preset 3D MobileNetV2 and attention blocks has been developed by Chinnam SK, Sistla V, and Kolli VK by implementing 3D brain imaging data 30 .Comparative analysis with other pertinent strategies using the BRATS dataset are stated in Table 1.Advanced optimization techniques like stochastic gradient descent and adaptive learning rate algorithms are being used to improve the performance of modified U-Net models for BTS.The integration of attention mechanisms, residual blocks, and advanced optimization techniques has led to significant improvements in segmentation accuracy and performance.
We recognize that our proposed blocks leverage combinations of existing methodologies, such as MA-Blocks, FCN blocks, and attention mechanisms.However, the true innovation of our work lies in the specific integration and optimization of these blocks within a unified framework tailored for the segmentation of brain tumors.Our IC-Net framework strategically combines these techniques to enhance feature extraction, improve segmentation accuracy, and address specific challenges in medical image analysis that individual blocks alone might not solve as effectively.This integrated approach results in a synergistic effect that significantly improves the performance metrics, as demonstrated in our comprehensive experimental results.We believe that this novel integration and its application to the specific domain of BTS contribute meaningful advancements to the field, providing a robust and effective tool for clinical use.

Dataset
The benchmark datasets used in this study are covered in this section's discussion.The BraTS2020 dataset, obtained from Kaggle, serves as the basis for our system's analysis and training.Four MRI sequences are collected for each patient: fluid attenuated inversion recovery (FLAIR), T1-contrast-enhanced (T1ce), T1-weighted (T1), and T2-weighted (T2), along with the associated data sample shown in Fig. 1.The training brains come with ground truth for which 5 segmentation labels are provided, namely non-tumor, necrosis, edema, non-enhancing tumor and enhancing tumor.The experts assigned labels to the presented ground truths.Each 3D volume has 155 2D slices/images of brain MRIs that were gathered from different parts of the brain.All brains in the dataset have the same orientation.In NIfTI format, each slice has a size of 240 × 240 pixels and is composed of single- channel grayscale pixels.Table 2 provides a summary of the dataset.

Pre-processing
Preprocessing methods get the medical images ready for segmentation task training on a DL model are discussed.
In order for the model to gain knowledge from the data during training, it must be in an appropriate format and contain relevant information.Data pre-processing is required to eliminate noisy regions and extract crucial segmentation properties before importing the dataset into the training model, ensuring clear labeling.The nib library is used for loading medical image data from NIfTI files, while data shuffling and data resizing are used for training models.Class mapping and One-Hot Encoding are used for segmentation tasks.Normalization

Methodology
In this manuscript, we introduce IC-Net model that incorporates MA-blocks, attention blocks and FCN block.
Our contributions can be summarized as follows: The image segmentation operation of the IC-Net architecture is discussed in detail here (Fig. 2).A sample image of seven dots is taken for illustration.The image proceeds through a well-planned series of steps.The architecture starts with an input layer that accepts an image of specific dimensions followed by a five-block encoder component.An encoder block contains convolutional layers and max-pooling operations so that en Block (1 to 4) are capable of extracting hierarchical features from the input images.These convolutional layers capture complex features as we go deeper into the network, while the fifth encoder block represents the highest level of feature refinement.The operational representation of the MA Block is shown in Fig. 3.The cap-shaped structure above the block diagram, consisting of dotted lines connecting the input, MA Block, and output images, provides an overview of the flow block diagram's processes.It processes the input image with seven colored circles through operations, starting with a convolutional layer for spatial features, followed by batch normalization for stable training and ReLU activation.A second convolutional layer refines these features, and combined, these operations enhance the image's representation for segmentation.While the block doesn't directly alter the output image, it primes it for segmentation.Subsequent layers in the neural network use these features for circle   Followed by the operation of FCN, The cap-shaped structure above the Fig. 5, with dotted lines connecting the input from the convolution block which is given to up sampling, then given to attention and output images, provides a concise summary of the processes involved.The decoder part which consists of four blocks, each with a transpose convolutional layer, an attention mechanism, and a concatenation operation comes into play.The transpose convolutional layers are essential for boosting the spatial resolution of the feature maps, and attention algorithms make sure that upsampling is targeted and context-sensitive which refers to the ability of a model to adapt its processing based on the context or surroundings of the input data.This process involves: computing "theta" and "phi" features, combining them through ReLU activation to emphasize positive relationships, calculating attention weights with a sigmoid activation (ranging from 0 to 1), and multiplying these weights with the original image.This method enhances feature representation by emphasizing regions of interest, vital for accurate segmentation.It allows the network to focus on critical areas, aiding in distinguishing object boundaries from the background and ultimately enhancing segmentation accuracy.The fifth decoder block produces segmentation map and provide class probabilities for each pixel with the help of 1 × 1 convolutional layer and SoftMax activation.This convoluted process reaches its culmination and the operation of Attention Block is shown in Fig. 5.This analysis aids a profound understanding and a granular insight of the IC-Net's image segmentation functionality, workflow and its performance.Overall, this methodology facilitates image segmentation through feature extraction and its refinement to produce pixel-wise class predictions in the output segmentation map.The entire operation is illustrated in Algorithm-1.
Algorithm 1. Algorithm for IC-Net Model.Proposed methodology aims to enhance BTS accuracy by integrating MA-Blocks, FCN Block, and an attention mechanism within the IC-Net architecture as shown in Fig. 2.
We revisit the operations of MA block, FCN block and attention block functions with complete mathematical representation as shown below.

Convolution operation
The convolution operation given an input image X(i) is represented as where v 1 = v 2 = n + 2p − f + 1 ; , W is a kernel, n, size of input image, p is padding size and f is the filter size and v 1 , v 2 are the height and width of the output feature matrix respectively.The functionalities of each blocks are explained below with the corresponding mathematical descriptions.For the easiness of illustration, we drop the subscripts in the notation and it is assumed that the input image to the architecture of each block is assumed as X output as Y and the outputs of intermediate stages as Y 1 , Y 2 , Y 3 and so on respectively.
• MA Block The operation of MA block includes convolution between X(i) and W to give Y 1 as mentioned in (1) followed by a batch normalization, , ǫ is a very small positive number.Following this operation, there will be a ReLU activation • FCN Block The operation of the FCN block comprises several key steps.It starts with a convolution operation as mentioned in (1) followed by a ReLU(x) = max (0, x) activation function to give an intermediate output as Y 1 .The output Y 1 undergoes one more convolution as in (1) followed by a Tanh Activation to give a response Y 2 .In addition, one more convolution along with tanh is used in the next stage to give Y 3 .It is to be noted that tanh = 1−e −2x 1+e −2x and σ (z) = 1 1+e −z .After convolution with Tanh Activation, another convolution between Y 2 and W as in (1) followed by a σ activation function is applied to get Y 3 .Now all these are concatenated using Y 4 = Y 1 ||Y 2 ||Y 3 , where || denotes column wise concatenation.Final response Y is obtained by doing another convolution between Y 4 and W .In these equations, X and Y represents the input and output feature matrix respectively.Y 1 , Y 2 , Y 3 and Y 4 represent output after convolution with Tanh, σ and ReLU respectively.Y 4 denoted concatenated feature matrix.
• Attention Block It encompasses several integral components and operations.We have the input feature matrix T(i) , along with an additional input feature matrix P(i) .Output feature matrix B is the result of the convolution operation, while the application of attention weights yields the output feature matrix Y .In this Operations, Firstly theta convolution operation T = T(i) • W takes place as in (1).Similar operation takes place to operate on P in Phi convolution.These results are added together to yield A , with a subsequent appli- cation of the ReLU activation function.Output from the addition is processed further through the operation B = A(i) • W as in (1).Finally, the output with attention is obtained as Y = B • T.An attention mechanism is used to emphasize critical spatial information between two feature maps, focusing on relevant parts of the input image while suppressing irrelevant information consequently enhancing segmentation accuracy.This mechanism, strategically placed within the decoder part of the model, enhances the network's ability to focus on critical regions.The mechanism involves specific steps such as 1x1 convolutions, element-wise addition, ReLU activation, dimensionality reduction, and the creation of an attention mask.Organizes an elegant attention performance by incorporating input feature maps and contextual data.It directs the network's attention, improving its interpretive abilities.Provides neural networks with specific focus capabilities.Perpetually allocates attention to regions within the input space, enhancing interpretative prowess and predictive accuracy.The integration of these components, MA-Blocks, FCN blocks, and the attention mechanism, forms a robust Impact of IC NET -Our IC-Net framework presents a number of novel developments, each of which adds in a different way to its improved usefulness and speed.Our architecture's MA block uses a simple yet efficient series of convolutional, batch normalization, and activation layers to facilitate deeper network representation while streamlining feature extraction.Our FCN block, on the other hand, is unique in that it uses parallel convolutional layers with several activation functions (ReLU, tanh, and sigmoid) to enable the combination of multiple feature representations in a single block.In the meantime, the attention block integrates a complex channel attention mechanism that enables the model to selectively emphasize important characteristics across channels.It does this by using gating signals and convolutions on input feature maps to build attention maps.Our architecture has been shown superior through extensive testing and comparative analyses, which reveal its higher performance in tasks like classification and segmentation on benchmark datasets.Feature map and attention mechanism visualizations demonstrate how well our architecture can capture complex patterns and highlight important regions, while thoughtful design decisions in each block address particular shortcomings in traditional systems.These developments, in conjunction with use case cases in highly specialized areas such as remote sensing and medical picture segmentation, establish our IC-Net as a cutting edge and very efficient solution for image analysis work.

Results and discussion
For the implementation, we utilized PyCharm as our development environment.system configuration was centered around a PowerEdge R740 server equipped with an INTEL XEON Silver 4208 2.1 GHz processor, a Tesla V100 GPU boasting 8 cores and 16 threads, delivering 9.6 gigapixels per second (GP/s) of graphics processing power, and backed by 128 GB of DDR4 RAM.We import TensorFlow, a deep learning framework, and Keras, a high-level API for model construction.It includes layers like dense, convolutional, and recurrent, and the Model class for defining and configuring deep learning models.It serves as a foundational step for building and training neural networks for various machine learning tasks.According to the experimental findings, IC-Net performs better at tumor segmentation than conventional U-Net models.Utilizing criteria like accuracy, sensitivity, specificity, and precision, the model's performance is assessed.To establish the optimal segmentation performance, it is trained on the brain MRI dataset BraTS2020.During our experimental time, we encountered constraints, choosing an optimizer and computational resources.Switching to GPU-based training was necessary for increased effectiveness.The Adam optimizer was chosen due to flexibility.Addressing hyperparameter alteration, data constraints, and model complexity is crucial.

Model training parameters training approach
Using categorized training data we trained our model using the Adam optimizer with a learning rate of 0.001 and the loss function as Categorical Crossentropy.Measures used to calculate the performance of the model includes: Accuracy, Precision, Sensitivity, Specificity, DSC (core, whole, and enhancing tumors), Tversky, Focal and boundary loss.Important detailing of the dataset used have been discussed above in section "Dataset" and Important basics of our technique on preprocessing processes to the dataset in section "Pre-processing".These details were vital for guaranteeing the repeatability of our findings and enabling a thorough evaluation of our IC-Net methodology.With four different MRI sequences, the model suggested in this research is separately trained and validated four times.Hyper-parameters used for training the network are described in Table 3.The number of floating-point operations (FLOPs) for a convolutional layer is calculated as twice the product of the number of kernels, the kernel height, the kernel width, the output height, and the output width.Number of FLOPs for a fully connected layer is calculated as twice the product of the input size and the output size.Using the above details, the total FLOPs for the MA Block can be calculated by multiplying twice the kernel size by the input channels, output channels, height, and width.For the Attention Block, the total FLOPs involve the sum of two sets of operations: the convolutional layers and the subsequent addition, activation, and multiplication operations.The total FLOPs for the FCN (Fully Convolutional Network) Block are determined by tripling twice the kernel size and multiplying by the input channels, output channels, height, and width.With the above reference and given assumptions, the FLOPs for the MA Block, Attention Block, and FCN Block were calculated to be about 2,147,483,648 FLOPs, 3,221,225,472 FLOPs, and 4,294,967,296 FLOPs, respectively.Total FLOPs for the model were determined to be around 9,663,676,416 FLOPs.Our system requirement me have mentioned it in section "Results and discussion" results and discussion.The FLOPs per second for our Tesla V100 GPU is rounded 9.66 GFLOPs/s (gigaflops per second).Time consumption can be calculated when Number of Training Samples is multiplied with number of Epochs and time per epoch and when divided with number of GPU cores will give the training time.The assessed training period, calculated based on the number of GPU cores, number of training samples, number of epochs and the time per epoch, is about 3112.5 s.For calculating memory consumption be Trainable params which can be added with non-trainable params multiplied by size of parameter in bytes.

Evaluation metrics
Accuracy, sensitivity, precision and specificity are used to evaluate the model based on the provided segmented ground truth of the tumor part in the MRI are then calculated using following Eqs.( 2), ( 3), ( 4), ( 6) and (7).By dividing the total number of predictions by the number of right predictions, accuracy processes determine how exact each prediction is.The precision technique highlights exactness by calculating the percentage of true positive predictions among all positive forecasts.Remember processes the percentage of all real positive cases that are accurate positive predictions, with an emphasis on completeness.
DSC score performance metric computes the similarity percentage between the ground truth and the output of a model.Suppose, C and D are two sets, the dice similarity of these two sets are then calculated with Eq. ( 5) Sensitivity is calculated with Eq. ( 6) where, cardinalities of sets C and D are denoted with |C| and |D| respectively, G 1 representing the proportion of tumor regions of ground truth images and C 1 represents tumor regions that were predicted by the model.
Specificity is calculated with Eq. ( 7) where G 0 represents non-tumor tissue regions of the ground truth and C 0 represents the non-tumor tissue regions predicted by the model.

Model evaluation
In the results section of our research paper, we are delighted to report exceptionally high-performance metrics obtained after training our model for 50 and 100 epochs, underscoring the effectiveness of our approach.For the 50-epoch experiment, we achieved accuracy rates consistently exceeding 99.39%, coupled with low loss value of 0.0246, indicating the model's strong predictive capabilities and the minimization of errors.Precision scores consistently above 99.43%demonstrate the model's proficiency in correctly identifying positive cases while keeping false positives to a minimum.Sensitivity consistently exceeding 99.26%.This indicates the model's ability to successfully capture a significant proportion of true positive cases.Specificity above 99.79%,reflecting its capability to effectively distinguish between negative and positive instances.Results for 50 epoch are displayed in Figs. 6 and 7.For the 100-epoch experiment, our model consistently achieved accuracy rates exceeding 99.65%, maintained low loss values of 0.0159, and demonstrated precision scores of 99.58%, minimizing false positives.Moreover, our research exhibited high sensitivity exceeding 99.44%, indicating the model's ability to capture true positive cases, and specificity consistently of 99.86%, highlighting its proficiency in distinguishing negative instances.Remarkably, these high-performance metrics remained stable and even improved marginally in the 100-epoch experiment, demonstrating the robustness and reliability of our methodology over extended training periods are given in Figs. 8 and 9. Our model consistently outperforms advanced methods in performance metrics, demonstrating its superiority in various aspects.Key findings summarize these results in Table 4 and results when compared with existing methods are given in Fig. 16 in a graphical way.To make sure that our results were reliable and consistent, we ran the model several times during our studies.These numerous runs yielded the following accuracy values: 099636, 099635, 099664, 099667, 099635, 099664, 099667, and 099635.We determined the standard deviation of these accuracy numbers in order to further verify the stability of our framework.Performance of our model displays a high degree of consistency and reliability, as evidenced by the standard deviation of 0.00014.This low standard deviation supports the effectiveness of our method by showing that the accuracy of our model is both high and consistent across runs.Our research demonstrated the effectiveness of our approach, with high accuracy, low loss, and notable precision, sensitivity, and specificity scores, indicating its reliability and potential contributions.Performance of our IC-Net resuts are presented in Table 5.The results provide a solid foundation for our research paper's conclusions, demonstrating the effectiveness of our methodology in achieving our intended goals.Our analysis reveals that our approach outperforms 43,44 in the segmentation of brain tumors using the BraTS2020.Figures 10 and 11 represent the prediction using existing models which outperforms by our IC-Net.Figures 12 and 14 shows the segmentation result of brain tumors using Brats 2021 and 2019 data (Figs.13,  14).The superior results are evident in the accuracy of delineating tumor classes and individual core, whole, and enhancing tumors, showcased in columns four, five, and six of the images.Tables 6, 7 and 8 provides a comparison of the DSC values of 2020, 2021 and 2019 brats data for Whole Tumor (WT), Tumor Core (TC), and Enhanced Tumor (ET) with the most recent algorithms.In addition, the graphical depiction of the outcomes for  WT, TC, and ET is shown in Fig. 15.Results of our IC-Net shows promising visuals which are presented below in Fig. 13.Our model effectively defines data boundaries, improving segmentation quality and precision.It yields more accurate, visually appealing results, highlighting its practical value in real-world applications for improved decision-making processes for clinical applications and medical image analysis (Fig. 16).

Conclusion
In summary, IC-Net is a novel semantic segmentation architecture that combines MA-blocks, FCN, attention blocks and an encoder-decoder structure.It achieves state-of-the-art results in segmentation tasks by effectively capturing complex features and context information.Our manuscript provides a comprehensive explanation of the IC-Net framework and its constituent components, along with experimental results demonstrating its effectiveness.Our results of tumor segmentation are influenced differently by many variables.This research suggests a multimodal BTS method based on IC-Net to make better use of multimodal brain tumor image data.Through wide experimentations and quantitative evaluations, we validate the superiority of our IC-Net model compared to other state-of-the-art segmentation methods.Our proposed modifications have proven highly accurate and reliable in achieving BTSs, even in challenging scenarios with irregular tumor shapes and variable appearances.Figure 11.This image displays existing results, serving as a point of comparison with the visualization of qualitative results on BraTS2020 MRI sequences in 44 .

Future directions
We outline several paths for future research, including model refinement, domain adaptation, and real-time clinical deployment.Outline the development and evaluation of IC-Net, a modified neural network model for BTS.Through rigorous experiments, we demonstrate that IC-Net offers superior performance compared to traditional U-Net and other existing models.Subsequent investigations will concentrate on incorporating IC-Net into clinical procedures, guaranteeing its usability and efficacy in practical contexts.The model will be created with ease of integration into current systems, enabling real-time tumor segmentation and supporting the choice of diagnosis and course of treatment.Performance evaluations will take place in actual clinical settings, and input from medical professionals will be vital to improving the model.Other medical imaging tasks will also be added to IC-Net, which could enhance patient outcomes and diagnostic precision.To improve IC-Net's usefulness in medical imaging, future studies should examine how it may be integrated into clinical workflows, evaluate how well it works in practical situations, and extend its use to additional medical imaging modalities like PET, CT, and ultrasonography images.Our research contributes to the ongoing efforts to enhance medical image analysis, aiming to improve patient care and treatment outcomes in neuro-oncology.

Figure 2 .Figure 3 .
Figure 2. The workflow block diagram of the IC-Net architecture model.

Figure 4 .
Figure 4.This detailed image provides an operational representation of the FCN Block.Input image, featuring a composed of seven distinct colors.

Figure 5 .
Figure 5.This detailed image provides an operational representation of the Attention Block.
all in sequence.In these expressions, X denotes input feature matrix, W represent the filter used in the convolution, Y 4 denotes output image and Y 1 , Y 2 , & Y 3 denote intermediate output features respectively.

Figure 6 .
Figure 6.Accuracy and loss when trained for 50 Epoch.

Figure 7 .
Figure 7. Precision, sensitivity and specificity when trained for 50 Epoch.

Figure 8 .
Figure 8.Our model achieved 99.6% accuracy in 100 Epoch training, despite a cluttered visual representation due to the intricate level of detail beyond the 99.Model's loss value of 0.0159 resulted in a cluttered appearance due to the intricate level of detail.

Figure
Figure Precision, specificity when trained for 100 Epoch.Our IC-Net has achieved high precision, sensitivity, and specificity values, resulting in a detailed presentation with performance metrics beyond typical ranges.

Figure 10 .
Figure 10.This image displays existing results, serving as a point of comparison with the visualization of qualitative results on BraTS2020 MRI sequences in 43 .

Figure 12 .
Figure 12.This image displays visualization of qualitative results on BraTS2021.

Figure 13 .
Figure 13.After training the model, the following visualization showcases the qualitative results of the IC-Net model on BraTS2020 MRI sequences.

Figure 14 .
Figure 14.After training the model, the following visualization showcases the qualitative results of the IC-Net model on BraTS2019 MRI sequences.

Figure 15 .
Figure 15.Following visualization shows the subclass results of the IC-Net model on BraTS2020 MRI Data.

Figure 16 .
Figure 16.Comparison results of Our Model compared with existing methods.

Table 1 .
Comparative analysis with other pertinent strategies using the BRATS dataset.

mechanism Features Advantages Effectiveness in segmentation tasks
ensures pixel values fall within a similar range, while data yielding generates batches of preprocessed data for training and evaluation.

Table 2 .
Summary of the dataset.
Firstly, MA-Blocks inspired by recent advancements in attention mechanisms are integrated into IC-Net to focus on pertinent tumor regions within the input, incorporating both global and local context information.The MA-Block function, a vital component of IC-Net, is detailed, encompassing specific operations such as a 3 × 3 convolutional layer, batch normalization, ReLU activation, and another convolutional layer.The architecture of these MA-blocks allows for adaptive feature map weighting, effectively capturing intricate patterns within the data.It improves spatial integrity and capturing delicate features for subsequent layers.Also, ensures that it identifies detailed patterns in input data using convolutional layers, batch normalization, and ReLU activation, improving spatial integrity and capturing delicate features for subsequent layers.These blocks play a pivotal role in the encoder section, aiding in the extraction of hierarchical features from the input image across various scales through progressive down-sampling via max-pooling layers.The number of filters employed in each convolutional block is indicated by the numerals(32, 64, 128, 256, 512).Furthermore, FCN are integrated within IC-Net to capture multi-scale information and enhance the network's capability to handle tumors of varying sizes and shapes.The FCN blocks are thoroughly explained, emphasizing the concatenation of features derived from methodology for BTS.The model's segmentation accuracy is enhanced by utilizing attention mechanisms, diverse activation functions, and adaptive feature weighting to enhance feature representation.

Table 3 .
Hyperparameters used for training the network.Our IC-Net memory consumption is estimated based on the sum of trainable and non-trainable parameters; it approximately sums to 63.23 megabytes.

Table 4 .
On the BraTS2020 dataset, a comparison of IC-Net with state-of-the-art techniques.

Table 5 .
Performance of our IC-Net.

Table 6 .
Performance comparisons of subclass tumor using Brats2020 data.

Table 7 .
Performance comparisons of subclass tumor using Brats2021 data.

Table 8 .
Performance comparisons of subclass tumor using Brats2019 data.